A colored operad for string link infection

Budney recently constructed an operad that encodes splicing of knots. He further showed that the space of (long) knots is generated over this operad by the space of torus knots and hyperbolic knots, thus generalizing the satellite decomposition of knots from isotopy classes to the level of the space of knots. Infection by string links is a generalization of splicing from knots to links. We construct a colored operad that encodes string link infection. We prove that a certain subspace of the space of 2-component string links is generated over a suboperad of our operad by its subspace of prime links. This generalizes a result from joint work with Blair from isotopy classes of knots to the space of knots. Furthermore, all the relations in the monoid of 2-string links (as determined in our joint work with Blair) are captured by our infection operad.Comment: Some changes and corrections, mostly suggested by the referee. 38 pages, 14 figures. To appear in Algebr. & Geom. Topo

Incorporating System-Level Objectives into Recommender Systems

One of the most essential parts of any recommender system is personalization-- how acceptable the recommendations are from the user's perspective. However, in many real-world applications, there are other stakeholders whose needs and interests should be taken into account. In this work, we define the problem of multistakeholder recommendation and we focus on finding algorithms for a special case where the recommender system itself is also a stakeholder. In addition, we will explore the idea of incremental incorporation of system-level objectives into recommender systems over time to tackle the existing problems in the optimization techniques which only look for optimizing the individual users' lists.Comment: arXiv admin note: text overlap with arXiv:1901.0755

Weighted Random Walk Sampling for Multi-Relational Recommendation

In the information overloaded web, personalized recommender systems are essential tools to help users find most relevant information. The most heavily-used recommendation frameworks assume user interactions that are characterized by a single relation. However, for many tasks, such as recommendation in social networks, user-item interactions must be modeled as a complex network of multiple relations, not only a single relation. Recently research on multi-relational factorization and hybrid recommender models has shown that using extended meta-paths to capture additional information about both users and items in the network can enhance the accuracy of recommendations in such networks. Most of this work is focused on unweighted heterogeneous networks, and to apply these techniques, weighted relations must be simplified into binary ones. However, information associated with weighted edges, such as user ratings, which may be crucial for recommendation, are lost in such binarization. In this paper, we explore a random walk sampling method in which the frequency of edge sampling is a function of edge weight, and apply this generate extended meta-paths in weighted heterogeneous networks. With this sampling technique, we demonstrate improved performance on multiple data sets both in terms of recommendation accuracy and model generation efficiency

No Margin for Error: A Study of Two Women Balancing Motherhood and Ph.D. Studies

This cogenerative ethnography explored the lived experiences of two graduate students balancing Ph.D. studies and motherhood through McClusky’s (1963) Theory of Margi n. Specifically, we asked ourselves: What impact does pregnancy have on personal and academic selves and how are multiple roles and responsibilities managed? Through an analysis of dialogues, artifacts, conceptual maps, and narratives, examples of internal and external lo ad revealed the dynamic nature of the female experiences in graduate school. Excerpts from the data showed how roles, relationships, and experiences are characterized and how similar or different those example s were, given individual context. Implications of this research for students, faculty, and higher education policy are explored